VISE

Abstract

We present VISE, or Vehicle Image Search Engine, to support the fast search of similar vehicles from low-resolution traffic camera images. VISE can be used to trace and locate vehicles for applications such as police investigations when high-resolution footage is not available. Our system consists of three components: an interactive user-interface for querying and browsing identified vehicles; a scalable search engine for fast similarity search on millions of visual objects; and an image processing pipeline that extracts feature vectors of objects from video frames. We use transfer learning technique to integrate state-of-the-art Convolutional Neural Networks with two different refinement methods to achieve high retrieval accuracy. We also use an efficient high-dimensional nearest neighbor search index to enable fast retrieval speed. In the demo, our system will offer users an interactive experience exploring a large database of traffic camera images that is growing in real time at 200K frames per day.